class_docstr: PyTorch Lightning trainer for cnntransformer.
  
  Args:
      feature_extractor (torch.nn.Sequential, optional): the feature extractor network.
      optimizer (dict): optimizer parameters.
      lr_milestones (list): list of epoch indices. Must be increasing.
      lr_gamma (float): multiplicative factor of learning rate decay.
docstr: Forward pass for the model with a feature extractor and a classifier.
docstr_tok: ["Forward","pass","for","the","model","with","a","feature","extractor","and","a","classifier","."]
code: def forward(self, x):
      """Forward pass for the model with a feature extractor and a classifier."""
      x = self.feat(x)
      output = self.classifier(x)
      return output
  
imports: import pytorch_lightning
  import torch
  import torch.nn
  from sklearn.metrics import accuracy_score
  from kale.predict import losses
label_desc: Use image node_id 2 for calling the CNNTransformerTrainer obj's underlying member method code with example usage: obj.forward(x) and returns: output